{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute this cell to install dependencies\n",
    "%pip install sf-hamilton[visualization]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the individua modules within Jupyter [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/scikit-learn/species_distribution_modeling/hamilton_notebook.ipynb) [![GitHub badge](https://img.shields.io/badge/github-view_source-2b3137?logo=github)](https://github.com/apache/hamilton/blob/main/examples/scikit-learn/species_distribution_modeling/hamilton_notebook.ipynb)\n",
    "\n",
    "\n",
    "Add `--display --write_to_file` to visualize and write to the same named .py file. This will override existing files!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the jupyter extension\n",
    "%load_ext autoreload\n",
    "# set it to only reload the modules imported\n",
    "%autoreload 1\n",
    "%reload_ext hamilton.plugins.jupyter_magic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cell_to_module load_data\n",
    "\n",
    "from sklearn.datasets import fetch_species_distributions\n",
    "from sklearn.utils._bunch import Bunch\n",
    "\n",
    "\n",
    "def data() -> Bunch:\n",
    "    return fetch_species_distributions()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cell_to_module grids\n",
    "\n",
    "from typing import Tuple\n",
    "\n",
    "import numpy as np\n",
    "import numpy.typing as npt\n",
    "from original_script import construct_grids\n",
    "from sklearn.utils._bunch import Bunch\n",
    "\n",
    "from hamilton.function_modifiers import pipe_input, step\n",
    "\n",
    "\n",
    "def _construct_grids(batch: Bunch) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:\n",
    "    \"\"\"Our wrapper around and external function to integrate it as a node in the DAG.\"\"\"\n",
    "    return construct_grids(batch=batch)\n",
    "\n",
    "\n",
    "@pipe_input(step(_construct_grids))\n",
    "def data_grid_(\n",
    "    data: Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]],\n",
    ") -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:\n",
    "    return data\n",
    "\n",
    "\n",
    "def meshgrid(\n",
    "    data_grid_: Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]],\n",
    ") -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:\n",
    "    return np.meshgrid(data_grid_[0], data_grid_[1][::-1])\n",
    "\n",
    "\n",
    "def land_reference(data: Bunch) -> npt.NDArray[np.float64]:\n",
    "    return data.coverages[6]\n",
    "\n",
    "\n",
    "def idx(land_reference: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:\n",
    "    return np.where(land_reference > -9999)\n",
    "\n",
    "\n",
    "def coverages_land(data: Bunch, idx: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:\n",
    "    return data.coverages[:, idx[0], idx[1]].T\n",
    "\n",
    "\n",
    "def background_points(data: Bunch) -> npt.NDArray[np.float64]:\n",
    "    np.random.seed(13)\n",
    "    return np.c_[\n",
    "        np.random.randint(low=0, high=data.Ny, size=10000),\n",
    "        np.random.randint(low=0, high=data.Nx, size=10000),\n",
    "    ].T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cell_to_module preprocessing\n",
    "\n",
    "from typing import Dict, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import numpy.typing as npt\n",
    "from original_script import create_species_bunch\n",
    "from sklearn.utils._bunch import Bunch\n",
    "\n",
    "from hamilton.function_modifiers import extract_fields, pipe_input, source, step\n",
    "\n",
    "\n",
    "def _create_species_bunch(\n",
    "    species_name: str,\n",
    "    data: Bunch,\n",
    "    data_grid: Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]],\n",
    ") -> npt.NDArray[np.float64]:\n",
    "    \"\"\"Our wrapper around and external function to integrate it as a node in the DAG.\"\"\"\n",
    "    return create_species_bunch(\n",
    "        species_name, data.train, data.test, data.coverages, data_grid[0], data_grid[1]\n",
    "    )\n",
    "\n",
    "\n",
    "def _standardize_features(\n",
    "    species_bunch: npt.NDArray[np.float64],\n",
    ") -> Tuple[\n",
    "    npt.NDArray[np.float64],\n",
    "    npt.NDArray[np.float64],\n",
    "    npt.NDArray[np.float64],\n",
    "]:\n",
    "    mean = species_bunch.cov_train.mean(axis=0)\n",
    "    std = species_bunch.cov_train.std(axis=0)\n",
    "    return species_bunch, mean, std\n",
    "\n",
    "\n",
    "@extract_fields(\n",
    "    {\n",
    "        \"bunch\": Bunch,\n",
    "        \"mean\": npt.NDArray[np.float64],\n",
    "        \"std\": npt.NDArray[np.float64],\n",
    "        \"train_cover_std\": npt.NDArray[np.float64],\n",
    "        \"test_cover_std\": npt.NDArray[np.float64],\n",
    "    }\n",
    ")\n",
    "@pipe_input(\n",
    "    step(_create_species_bunch, data=source(\"data\"), data_grid=source(\"data_grid_\")),\n",
    "    step(_standardize_features),\n",
    ")\n",
    "def species(\n",
    "    chosen_species: Tuple[\n",
    "        npt.NDArray[np.float64],\n",
    "        npt.NDArray[np.float64],\n",
    "        npt.NDArray[np.float64],\n",
    "    ],\n",
    ") -> Dict[str, npt.NDArray[np.float64]]:\n",
    "    train_cover_std = (chosen_species[0].cov_train - chosen_species[1]) / chosen_species[2]\n",
    "    return {\n",
    "        \"bunch\": chosen_species[0],\n",
    "        \"mean\": chosen_species[1],\n",
    "        \"std\": chosen_species[2],\n",
    "        \"train_cover_std\": train_cover_std,\n",
    "        \"test_cover_std\": chosen_species[0].cov_test,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cell_to_module train_and_predict\n",
    "\n",
    "import numpy as np\n",
    "import numpy.typing as npt\n",
    "from sklearn import svm\n",
    "from sklearn.utils._bunch import Bunch\n",
    "\n",
    "from hamilton.function_modifiers import pipe_output, source, step, value\n",
    "\n",
    "\n",
    "# TODO: add another model or two and use `.when` to showcase that this can be customizable @execution\n",
    "def _OneClassSVM_model(\n",
    "    training_set: npt.NDArray[np.float64], nu: float, kernel: str, gamma: float\n",
    ") -> svm.OneClassSVM:\n",
    "    clf = svm.OneClassSVM(nu=nu, kernel=kernel, gamma=gamma)\n",
    "    clf.fit(training_set)\n",
    "    return clf\n",
    "\n",
    "\n",
    "def _decision_function(\n",
    "    model: svm.OneClassSVM,\n",
    "    underlying_data: npt.NDArray[np.float64],\n",
    "    mean: npt.NDArray[np.float64],\n",
    "    std: npt.NDArray[np.float64],\n",
    ") -> npt.NDArray[np.float64]:\n",
    "    return model.decision_function((underlying_data - mean) / std)\n",
    "\n",
    "\n",
    "def _prediction_step(\n",
    "    decision: npt.NDArray[np.float64], idx: npt.NDArray[np.float64], data: Bunch\n",
    ") -> npt.NDArray[np.float64]:\n",
    "    Z = decision.min() * np.ones((data.Ny, data.Nx), dtype=np.float64)\n",
    "    Z[idx[0], idx[1]] = decision\n",
    "    return Z\n",
    "\n",
    "\n",
    "@pipe_output(\n",
    "    step(_OneClassSVM_model, nu=value(0.1), kernel=value(\"rbf\"), gamma=value(0.5)),\n",
    "    step(\n",
    "        _decision_function,\n",
    "        underlying_data=source(\"coverages_land\"),\n",
    "        mean=source(\"mean\"),\n",
    "        std=source(\"std\"),\n",
    "    ),\n",
    "    step(_prediction_step, idx=source(\"idx\"), data=source(\"data\")),\n",
    ")\n",
    "def prediction_train(train_cover_std: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:\n",
    "    return train_cover_std\n",
    "\n",
    "\n",
    "@pipe_output(\n",
    "    step(_OneClassSVM_model, nu=value(0.1), kernel=value(\"rbf\"), gamma=value(0.5)),\n",
    "    step(\n",
    "        _decision_function,\n",
    "        underlying_data=source(\"test_cover_std\"),\n",
    "        mean=source(\"mean\"),\n",
    "        std=source(\"std\"),\n",
    "    ),\n",
    ")\n",
    "def prediction_test(train_cover_std: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:\n",
    "    return train_cover_std\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cell_to_module postprocessing_results\n",
    "\n",
    "from typing import Any, Dict, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import numpy.typing as npt\n",
    "from sklearn import metrics\n",
    "from sklearn.utils._bunch import Bunch\n",
    "\n",
    "from hamilton.function_modifiers import pipe_input, source, step\n",
    "\n",
    "\n",
    "def _normalize(\n",
    "    data: npt.NDArray[np.float64], land_reference: npt.NDArray[np.float64]\n",
    ") -> npt.NDArray[np.float64]:\n",
    "    data[land_reference == -9999] = -9999\n",
    "    return data\n",
    "\n",
    "\n",
    "@pipe_input(step(_normalize, land_reference=source(\"land_reference\")))\n",
    "def prediction_background(\n",
    "    prediction_train: npt.NDArray[np.float64], background_points: npt.NDArray[np.float64]\n",
    ") -> npt.NDArray[np.float64]:\n",
    "    return prediction_train[background_points[0], background_points[1]]\n",
    "\n",
    "\n",
    "def levels(prediction_train: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:\n",
    "    return np.linspace(prediction_train.min(), prediction_train.max(), 25)\n",
    "\n",
    "\n",
    "def area_under_curve(\n",
    "    prediction_test: npt.NDArray[np.float64],\n",
    "    prediction_background: npt.NDArray[np.float64],\n",
    ") -> float:\n",
    "    scores = np.r_[prediction_test, prediction_background]\n",
    "    y = np.r_[np.ones(prediction_test.shape), np.zeros(prediction_background.shape)]\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n",
    "    roc_auc = metrics.auc(fpr, tpr)\n",
    "    return roc_auc\n",
    "\n",
    "\n",
    "def plot_species_distribution(\n",
    "    meshgrid: Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]],\n",
    "    prediction_train: npt.NDArray[np.float64],\n",
    "    land_reference: npt.NDArray[np.float64],\n",
    "    levels: npt.NDArray[np.float64],\n",
    "    bunch: Bunch,\n",
    "    area_under_curve: float,\n",
    ") -> Dict[str, Any]:\n",
    "    return {\n",
    "        \"X\": meshgrid[0],\n",
    "        \"Y\": meshgrid[1],\n",
    "        \"Z\": prediction_train,\n",
    "        \"land_reference\": land_reference,\n",
    "        \"levels\": levels,\n",
    "        \"species\": bunch,\n",
    "        \"roc_auc\": area_under_curve,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Execute from here onwards to see results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We build the DAG and can visualize it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<title>prediction_background.with_normalize&#45;&gt;prediction_background</title>\n",
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       "<title>prediction_train&#45;&gt;levels</title>\n",
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       "<g id=\"node33\" class=\"node\">\n",
       "<title>function</title>\n",
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       "<!-- output -->\n",
       "<g id=\"node34\" class=\"node\">\n",
       "<title>output</title>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7fba100c7ee0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hamilton import driver\n",
    "import grids, load_data, postprocessing_results, preprocessing, train_and_predict\n",
    "\n",
    "dr = driver.Builder().with_modules(grids, load_data, postprocessing_results, preprocessing, train_and_predict).build()\n",
    "dr.visualize_execution(inputs={\"chosen_species\": \"aaa\"}, final_vars = [\"plot_species_distribution\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing the driver and requesting the desired nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Modeling distribution of species 'bradypus_variegatus_0'\n",
      "\n",
      " Area under the ROC curve : 0.868443\n",
      "________________________________________________________________________________\n",
      "Modeling distribution of species 'microryzomys_minutus_0'\n",
      "\n",
      " Area under the ROC curve : 0.993919\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from run import plot_helper\n",
    "\n",
    "species=(\"bradypus_variegatus_0\", \"microryzomys_minutus_0\")\n",
    "for i, name in enumerate(species):\n",
    "    print(\"_\" * 80)\n",
    "    print(\"Modeling distribution of species '%s'\" % name)\n",
    "    inputs = {\"chosen_species\": name}\n",
    "    final_vars = [\"plot_species_distribution\"]\n",
    "    results = dr.execute(inputs=inputs,final_vars=final_vars)[final_vars[0]]\n",
    "    plot_helper(i=i,**results)\n",
    "    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New feature we can use `@mutate` for distributed function output transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "%%cell_to_module train_and_predict_using_mutate\n",
    "import numpy as np\n",
    "import numpy.typing as npt\n",
    "from sklearn import svm\n",
    "from sklearn.utils._bunch import Bunch\n",
    "\n",
    "from hamilton.function_modifiers import mutate, apply_to, source, value\n",
    "\n",
    "\n",
    "def prediction_train(train_cover_std: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:\n",
    "    return train_cover_std\n",
    "\n",
    "\n",
    "def prediction_test(train_cover_std: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:\n",
    "    return train_cover_std\n",
    "\n",
    "\n",
    "@mutate(\n",
    "    prediction_train, \n",
    "    prediction_test, \n",
    "    nu=value(0.1), kernel=value(\"rbf\"), gamma=value(0.5)\n",
    ")\n",
    "def _OneClassSVM_model(\n",
    "    training_set: npt.NDArray[np.float64], nu: float, kernel: str, gamma: float\n",
    ") -> svm.OneClassSVM:\n",
    "    clf = svm.OneClassSVM(nu=nu, kernel=kernel, gamma=gamma)\n",
    "    clf.fit(training_set)\n",
    "    return clf\n",
    "\n",
    "@mutate(\n",
    "    apply_to(prediction_train,underlying_data=source(\"coverages_land\"),mean=source(\"mean\"),std=source(\"std\")),\n",
    "    apply_to(prediction_test,underlying_data=source(\"test_cover_std\"),mean=source(\"mean\"),std=source(\"std\")),\n",
    ")\n",
    "def _decision_function(\n",
    "    model: svm.OneClassSVM,\n",
    "    underlying_data: npt.NDArray[np.float64],\n",
    "    mean: npt.NDArray[np.float64],\n",
    "    std: npt.NDArray[np.float64],\n",
    ") -> npt.NDArray[np.float64]:\n",
    "    return model.decision_function((underlying_data - mean) / std)\n",
    "\n",
    "\n",
    "@mutate(\n",
    "    prediction_train,\n",
    "    idx=source(\"idx\"), data=source(\"data\")\n",
    ")\n",
    "def _prediction_step(\n",
    "    decision: npt.NDArray[np.float64], idx: npt.NDArray[np.float64], data: Bunch\n",
    ") -> npt.NDArray[np.float64]:\n",
    "    Z = decision.min() * np.ones((data.Ny, data.Nx), dtype=np.float64)\n",
    "    Z[idx[0], idx[1]] = decision\n",
    "    return Z\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<function prediction_train at 0x7f9fe23408b0>,) is from module train_and_predict_using_mutate\n",
      "________________________________________________________________________________\n",
      "Modeling distribution of species 'bradypus_variegatus_0'\n",
      "\n",
      " Area under the ROC curve : 0.868443\n",
      "________________________________________________________________________________\n",
      "Modeling distribution of species 'microryzomys_minutus_0'\n",
      "\n",
      " Area under the ROC curve : 0.993919\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from hamilton import driver\n",
    "import grids, load_data, postprocessing_results, preprocessing, train_and_predict, train_and_predict_using_mutate\n",
    "\n",
    "dr = (\n",
    "    driver.Builder()\n",
    "    .with_modules(\n",
    "        grids, \n",
    "        load_data, \n",
    "        postprocessing_results, \n",
    "        preprocessing, \n",
    "        train_and_predict, \n",
    "        train_and_predict_using_mutate\n",
    "        )\n",
    "    .allow_module_overrides()\n",
    "    .build()\n",
    "    )\n",
    "\n",
    "print(f\"{dr.list_available_variables()[-3].originating_functions} is from module {dr.list_available_variables()[-3].originating_functions[0].__module__}\")\n",
    "# dr.visualize_execution(inputs={\"chosen_species\": \"aaa\"}, final_vars = [\"plot_species_distribution\"])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from run import plot_helper\n",
    "\n",
    "species=(\"bradypus_variegatus_0\", \"microryzomys_minutus_0\")\n",
    "for i, name in enumerate(species):\n",
    "    print(\"_\" * 80)\n",
    "    print(\"Modeling distribution of species '%s'\" % name)\n",
    "    inputs = {\"chosen_species\": name}\n",
    "    final_vars = [\"plot_species_distribution\"]\n",
    "    results = dr.execute(inputs=inputs,final_vars=final_vars)[final_vars[0]]\n",
    "    plot_helper(i=i,**results)\n",
    "    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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